It's designed to work primarily with old fire insurance atlases (e.g. Sanborns) and is a bunch of hand-tuned heuristics. But it powers Building Inspector (http://buildinginspector.nypl.org), which is where all the data is validated consensus crowdsourcing, providing ground truth for the data coming out.
Unfortunately (or perhaps fortunately if you're trying to get a Computer Science PhD) I haven't come across anyone applying deep learning to map vectorization either. Frankly, it could open up a whole new field with respect to historical mapping (among other things).
Would love to talk more about it and see how deep learning could be applied here. We always structured the outputs of Building Inspector so they'd be useful training sets for unsupervised or reinforcement learning so it could hopefully apply here as well.